A modeling framework for quantifying spatial recruitment dynamics using abundance estimation and sibship analysis: code and simulation study output
Data files
Aug 26, 2024 version files 2.66 MB

README.md

SimResults_MultiTimeStep.csv

SimResults_singleCohort.csv
Abstract
Quantifying recruitment at the sibling group offers a powerful methodology for understanding densitydependent and environmental drivers of recruitment. We propose a modeling framework that combines sibship and abundance estimation datasets to estimate mean sibling group size, sibling group size process error, environmental and densitydependent effects on sibling group size, dispersal, and mortality rate. Geographic states in the model consist of discrete habitat patches connected via dispersal. Simulations were used to investigate the influence of sampling processes and sibling group size on parameter estimation within our modeling framework. Mean siblinggroup size, environmental effects on recruitment, and dispersal rate among habitat patches were estimated with high accuracy under a wide range of sampling conditions, including imprecise outofmodel estimates of capture probability and subsampling both within and among habitat patches. Densitydependent effects on recruitment and process error tended to be estimated with lower accuracy, though accuracy improved as sibling group size or sampling intensity increased. The main contribution of this research is a flexible quantitative modeling framework for parameterizing mechanistic models of recruitment dynamics with empirical sibship data.
README: A Modeling Framework for Quantifying Spatial Recruitment Dynamics Using Abundance Estimation and Sibship Analysis: Code and Simulation Study Output
https://doi.org/10.5061/dryad.2fqz612zd
Description of the data and file structure
The simulation results provided summarizes simulation output obtained using the associated software code provided (R scripts and Stan model code). The raw simulation output (stan model for each model run) was summarized by 1) extracting the parameter values and model diagnostics of interest from each stan model fit to a simulated dataset and 2) calculating the relative error and precision for each simulation.
Files and variables
File: SimResults_MultiTimeStep.csv
Description:
Variables
 mean: mean of the posterior distribution
 10%: 10th quantile of the posterior distribution
 90%: 90th quantile of the posterior distribution
 TrueSimValue: value used in the datagenerating simulation model
 posterior_sd: sd of the posterior distribution
 CICoverage: does the TrueSimValue fall between 10% and 90% (1=Yes; 0=No)
 CIPrecision: precision of the parameter estimate
 RMSE: rootmeansquareerror of the parameter estimate
 Bias: relative error of the parameter estimate
 maxRhat: maximum rhat from the stan model output
 min_n_eff: minimum effective sample size from the stan model output
 passchecks: did the model pass convergence checks? (1=Yes, 0=No)
 parm: parameter name
 simRep: simulation rep ID
 nSamples: number of samples per year
 p_sd: sampletosample variance in detection probability
 npatch: number of habitat patches in the simulated metapopulation
 nfg: number of family groups per year
 a: recruitment rate
 a_dense: densitydependent effects on recruitment rate
 a_sd: family group to family group variability in recruitment rate
 q: dispersal rate
 propPatchSurveyed: proportion of each sampled patch that was surveyed
 a_beta: environmental effects on recruitment rate
 captureProb_int: mean capture probability
 nFG_obs: number of family groups detected
 avg_n_per_fg: average number of captured individuals per family group
 total_catch_obs: total number of captured individuals
File: SimResults_singleCohort.csv
Description:
Variables
 mean: mean of the posterior distribution
 10%: 10th quantile of the posterior distribution
 90%: 90th quantile of the posterior distribution
 TrueSimValue: value used in the datagenerating simulation model
 posterior_sd: sd of the posterior distribution
 CICoverage: does the TrueSimValue fall between 10% and 90% (1=Yes; 0=No)
 CIPrecision: precision of the parameter estimate
 RMSE: rootmeansquareerror of the parameter estimate
 Bias: relative error of the parameter estimate
 maxRhat: maximum rhat from the stan model output
 min_n_eff: minimum effective sample size from the stan model output
 passchecks: did the model pass convergence checks? (1=Yes, 0=No)
 parm: parameter name
 simRep: simulation rep ID
 nSamples: number of samples per year
 p_sd: sampletosample variance in detection probability
 npatch: number of habitat patches in the simulated metapopulation
 nfg: number of family groups per year
 a: recruitment rate
 a_dense: densitydependent effects on recruitment rate
 a_sd: family group to family group variability in recruitment rate
 q: dispersal rate
 propPatchSurveyed: proportion of each sampled patch that was surveyed
 a_beta: environmental effects on recruitment rate
 captureProb_int: mean capture probability
 nFG_obs: number of family groups detected
 avg_n_per_fg: average number of captured individuals per family group
 total_catch_obs: total number of captured individuals
Code/software
R and the packages listed in the code are required to run the scripts.
SimulationSummaryandPlots_MultiTimeStep_CJFAS_R07042024.R: summarize and plot simulation results from Lewandoski and Brenden (2024)
SimulationSummaryandPlots_SingleCohortCJFAS.R: summarize and plot simulation results from Lewandoski and Brenden (2024)
sim_singleCohort.R: simulate datasets and estimate parameters using Stan (single year run for each cohort)
sim_multiTimeStep.R: simulate datasets and estimate parameters using Stan (multiyear run for each cohort)
multitimestep.stan: Stan model for multitime step model runs
singlecohort.stan: Stan model for single year model runs
SpatialPopDySimFunction_vDissertationChapter2.R: simulation function (called by sim_multiTimeStep.R)
SpatialPopDySimFunction_vDissertationChapter2_v_oneYr.R: simulation function (called by sim_singleCohort.R)
Methods
The simulation results were obtained using the code provided in the linked software related work (R code and Stan code provided).